Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	125,476
2	South Dakota	118,618
3	Utah	99,845
4	Rhode Island	98,590
5	Tennessee	97,154
6	Wisconsin	96,843
7	Iowa	96,329
8	Nebraska	93,620
9	Arizona	91,032
10	Kansas	88,969
11	Arkansas	88,685
12	Oklahoma	87,957
13	Indiana	87,074
14	Idaho	86,614
15	Alabama	85,154
16	Wyoming	84,506
17	Nevada	83,908
18	Illinois	83,817
19	Mississippi	83,392
20	Montana	83,015
21	Minnesota	78,662
22	Louisiana	78,483
23	New Mexico	77,166
24	Missouri	75,753
25	California	74,336
26	Georgia	73,133
27	South Carolina	73,021
28	Kentucky	72,634
29	Texas	72,304
30	Florida	72,077
31	Delaware	70,578
32	Alaska	69,938
33	Ohio	69,675
34	New Jersey	69,361
35	Massachusetts	66,728
36	Colorado	64,883
37	North Carolina	63,072
38	Connecticut	62,665
39	New York	62,362
40	West Virginia	59,509
41	Pennsylvania	59,408
42	Michigan	57,905
43	Maryland	53,083
44	Virginia	49,514
45	District of Columbia	46,957
46	New Hampshire	40,817
47	Washington	38,121
48	Puerto Rico	37,439
49	Oregon	31,129
50	Maine	24,386
51	Hawaii	16,973
52	Vermont	15,599

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	1,092
2	Utah	1,088
3	South Carolina	1,075
4	California	1,023
5	Kansas	978
6	Rhode Island	959
7	Kentucky	909
8	Texas	894
9	Arkansas	892
10	Oklahoma	891
11	Louisiana	854
12	Georgia	849
13	Massachusetts	841
14	New York	828
15	New Jersey	800
16	Delaware	780
17	North Carolina	736
18	Mississippi	697
19	Tennessee	695
20	Florida	691
21	Alabama	658
22	West Virginia	640
23	Indiana	628
24	Idaho	617
25	Ohio	613
26	New Mexico	609
27	Maine	608
28	Connecticut	596
29	Nevada	579
30	New Hampshire	576
31	Virginia	573
32	Pennsylvania	555
33	Nebraska	554
34	Illinois	501
35	Iowa	493
36	Wisconsin	493
37	Montana	482
38	Wyoming	482
39	Maryland	462
40	South Dakota	449
41	Missouri	431
42	Colorado	420
43	Alaska	419
44	District of Columbia	338
45	Michigan	326
46	Washington	303
47	North Dakota	282
48	Minnesota	277
49	Oregon	277
50	Vermont	260
51	Puerto Rico	159
52	Hawaii	108

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,287
2	New York	2,066
3	Massachusetts	1,959
4	Rhode Island	1,892
5	Connecticut	1,849
6	South Dakota	1,841
7	North Dakota	1,830
8	Mississippi	1,818
9	Louisiana	1,738
10	Illinois	1,568
11	Arizona	1,519
12	Pennsylvania	1,482
13	Michigan	1,455
14	Arkansas	1,412
15	Indiana	1,373
16	New Mexico	1,371
17	Iowa	1,349
18	Alabama	1,229
19	Tennessee	1,205
20	Kansas	1,202
21	Nevada	1,202
22	District of Columbia	1,193
23	South Carolina	1,172
24	Texas	1,110
25	Florida	1,108
26	Georgia	1,107
27	Missouri	1,078
28	Maryland	1,074
29	Minnesota	1,049
30	Delaware	1,028
31	Montana	1,016
32	Wisconsin	991
33	Nebraska	978
34	West Virginia	966
35	Colorado	940
36	Wyoming	901
37	Idaho	898
38	Ohio	860
39	California	833
40	North Carolina	762
41	Kentucky	759
42	Oklahoma	739
43	New Hampshire	667
44	Virginia	662
45	Puerto Rico	529
46	Washington	521
47	Utah	460
48	Oregon	417
49	Maine	354
50	Alaska	300
51	Vermont	261
52	Hawaii	222

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alabama	31
2	Kansas	28
3	Arizona	23
4	Pennsylvania	23
5	West Virginia	18
6	Nevada	16
7	South Dakota	16
8	Arkansas	15
9	California	15
10	Georgia	14
11	Mississippi	14
12	Tennessee	14
13	Connecticut	13
14	North Dakota	13
15	Texas	13
16	New Mexico	12
17	Massachusetts	11
18	Rhode Island	11
19	South Carolina	11
20	New Jersey	10
21	New York	10
22	Oklahoma	10
23	Illinois	9
24	North Carolina	9
25	District of Columbia	8
26	Florida	8
27	Idaho	8
28	Kentucky	8
29	Michigan	8
30	Indiana	7
31	Louisiana	7
32	Maryland	7
33	Minnesota	7
34	Nebraska	7
35	New Hampshire	7
36	Ohio	7
37	Oregon	7
38	Wisconsin	7
39	Maine	6
40	Missouri	6
41	Virginia	6
42	Colorado	5
43	Montana	5
44	Utah	5
45	Puerto Rico	4
46	Washington	4
47	Delaware	3
48	Iowa	3
49	Hawaii	2
50	Vermont	2
51	Alaska	1
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	285,431	1	99
Dewey	South Dakota	229,803	2	99
Bent	Colorado	226,645	3	99
Lincoln	Arkansas	226,198	4	99
Norton	Kansas	222,533	5	99
Davidson	Tennessee	112,758	269	91
Richland	South Carolina	74,012	1598	49
Orange	California	68,623	1856	40
York	South Carolina	67,122	1922	38
Pierce	Washington	35,144	2870	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Dickey	North Dakota	6,568	3	99
Grant	Nebraska	6,421	4	99
Iron	Wisconsin	6,330	5	99
Davidson	Tennessee	942	1865	40
Richland	South Carolina	873	1968	37
Orange	California	717	2218	29
York	South Carolina	715	2221	29
Pierce	Washington	459	2604	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.